Mr. Junting Chen Profile

Junting Chen

at University of Jinan

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Publications (3)

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Proceedings Article | 16 February 2022 Paper

SA-UNet for face anti-spoofing with depth estimation

Junting Chen, Sijie Niu, Xizhan Gao, Shenyuan Li, Jiwen Dong

Proceedings Volume 12083, 120831R (2022) https://doi.org/10.1117/12.2623137

KEYWORDS: Image segmentation, Image analysis, Neural networks, Cameras, Feature extraction, Convolution, Image processing, Computer programming

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With the large number of face recognition devices deployed in real application scenarios, face anti-spoofing has become a hot topic nowadays. Previous methods are mostly based on handcrafted features, while recent methods are mostly based on neural networks. However, both the traditional hand-crafted based method and the deep learning methods are still faced with the problem of insufficient generalization ability. In traditional deep learning methods for classification tasks, the label of samples is often a code of the category name. Recent studies have also shown that besides color and distortion, the depth information of face is also considered as an important clue to distinguish real and fake face. In order to combine prior knowledge of face depth information with deep learning method, it is a way worth exploring to expand the label information by using estimated depth image labels instead of coding labels. In this paper, we proposed an auxiliary supervised method to extend label information by using estimated depth information of face. A SA-UNet model which combined spatial attention modules with classic UNet is proposed to generate the depth estimation image for face anti-spoofing. Moreover, contrast depth loss is introduced to focus on the neighborhood information of the pixels, and a scoring method based on the proportion of non-background area is proposed to do the classification. In order to measure the generalization ability of our method, we choose CASIA FASD dataset, Idiap Replay Attack dataset and OULUNPU dataset for experimental verification. Experimental results show that our method is effective for face anti-spoofing task.

Proceedings Article | 27 January 2021 Paper

Automated geographic atrophy segmentation with multi-loss for SD-OCT images based on patient independent

Jiwen Dong, Junting Chen, Xizhan Gao, Rongbin Xu, Sijie Niu

Proceedings Volume 11720, 1172012 (2021) https://doi.org/10.1117/12.2589371

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The geographic atrophy (GA) caused by retinal layer atrophy is an important clinical manifestation of age- related macular disease (AMD). Automatic segmentation for GA in spectral-domain optical coherence tomography (SD-OCT) images is a challenging task. In this paper, we proposed a multi-loss convolutional neural network for GA automatic segmentation based on patient independent. Firstly, to overcome the shortness of samples in medical image processing, the proposed method augmented the samples with samples reversing. Then the model used multi-path block structure to replace single structure of classical CNN to enrich the diversity of features. And the multi-path block loss, cross entropy, and center loss were adapted to supervise and optimize the network effectively, thus it can force the network to learn more representative features. Finally, two data sets were used to evaluate the performance of the model, it demonstrated that the result has a high overlap ratio, correlation coefficient and low absolute area difference. The average overlap ratios on two data sets are 81.88% and 66.86% respectively.

Proceedings Article | 27 January 2021 Paper

Automated geographic atrophy segmentation with multi-loss for SD-OCT images based on patient independent

Jiwen Dong, Junting Chen, Xizhan Gao, Rongbin Xu, Sijie Niu

Proceedings Volume 11720, 1172012 (2021) https://doi.org/10.1117/12.2589371

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